Overview

Between September 10th and November 26th, 2019, embRACE LA:
* collected 542 online surveys from Angelinos who either live, work, or study in the city(i);
* held 11 conversations with city council members; and
* collected 112 pre-conversation and 80 post-conversation surveys from dinner participants.

The following reviews the results from the surveys and dinner conversations. Key takeaways include:
* Angelinos who participated in embRACE LA (either through the survye or dinner conversations) generally do not believe LA City is an equitable and inclusive place to live.
* Housing is viewed as a major barrier to creating greater equity as is racism and discrimination.
* Many do no trust the city to treat people equally or provide equal city services. * The overwhelming majority believe the city should prioritize equity and the great majority of embRACE dinner participants support an ORE.
* Community respondents and dinner participants prioritize activities for the ORE that involve tangible investments in community, like applying equitable budgeting tools to invest based on need, investing in local minority-owneded businesses, providing grants to local organizations working on equity, and hiring diverse city staff from local communities.

*(i)Note: A total of 567 community surveys were gathered on the website, but only 542 of these were from individuals who reported either living, working, or studying in LA City. This analysis does not include the 25 individuals without a connection to LA City.

Vision for an equitable LA City

During conversations, dinner participants imagined an equitable and inclusive LA City as a place with equal access to opportunity for all-placeholder

How equitable is LA City?

Only 22% of Angelinos who took the survey agreed or strongly agreed that LA City is an equitable and inclusive place to live for people of all races and ethnicities.
Only 26% of people attending our dinners agreed or strongly agreed with the same statement.
Community respondents most frequently ranked housing affordability, gentrification and displacement, and racism and discrimination as the top 3 barriers to creating a more equitable LA. In fact, over 50% of respondents thought housing affordability was one of the top 3 barriers to greater equity in LA City.
Dinner participants most frequently ranked housing affordability, income, and racism and discrimination as the top 3 barriers to creating a more equitable LA.
Over 50% of both community respondents and dinner participants thought housing affordability was one of the top 3 barriers to greater equity in LA.
30% or more of both community respondents and dinner participants thought racism and discrimination was one of the top 3 barriers to greater equity in LA.
About 3 in 4 of community respondents had little or no trust in the city to provide equal city services (X%) or equal opportunities to participate in decision-making for all people (X%).
Dinner participants were slightly more trusting of the city though than 50% of reported little trust or no trust at all in the city to treat people of all races and ethnicities equally equal city services, equal opportunities to participate in city decision-making, or generally equal treatment.

Is LA City equitable

What community members said

is_la_equitable <- dbGetQuery(con, "SELECT * FROM commsurvey_is_la_equitable") #grab view from potsgres 

#formating

is_la_equitable_count<-c('n=21', 'n=95', 'n=79', 'n=229', 'n=116')
equitable_x<-list(title="", 
                  tickformat="%")
equitable_y<-list(title="")

#graph

plot_ly(is_la_equitable, x=~pct , y=~fct_rev(factor(is_la_equitable, levels=is_la_equitable)), type = 'bar', text=is_la_equitable_count, orientation='h')%>%
  layout(title= str_wrap("LA City is an equitable and inclusive place to live for people of all races and ethnicities"), xaxis=equitable_x, yaxis=equitable_y, autosize=F)

What dinner participants said

pre_is_la_equitable <- dbGetQuery(con, "SELECT * FROM presurvey_is_la_equitable") #grab view from potsgres 

#formating

pre_is_la_equitable_freq<-c('n=5', 'n=23', 'n=17', 'n=48', 'n=18')
equitable_x<-list(title="", 
                  tickformat="%")

equitable_y<-list(title="")

#graph

plot_ly(pre_is_la_equitable, x=~percent , y=~fct_rev(factor(response, levels=response)), type = 'bar', text=pre_is_la_equitable_freq, orientation='h')%>%
  layout(title= str_wrap("LA City is an equitable and inclusive place to live for people of all races and ethnicities"), xaxis=equitable_x, yaxis=equitable_y)

Top barriers to equity

What community members said.

barriers_to_equity<-dbGetQuery(con, "SELECT response, freq, percent FROM commsurvey_barriers_to_equity WHERE percent > 26 OR percent < 6") 

  kable(barriers_to_equity, digits=0, col.names=c("Response","Frequency","Percent (%)"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    row_spec(1:3, bold = T, color ="white", background="#65B23E")%>%
    row_spec(4:8, bold=T, color= "white", background="#E53939")%>%
    add_header_above(c("What are the top 3 barriers to creating a more equitable and inclusive city?"=1," "=2))

What are the top 3 barriers to creating a more equitable and inclusive city?
Response	Frequency	Percent (%)
Housing affordability	310	57
Gentrification and displacement	201	37
Racism and discrimination	184	34
Public participation	29	5
Environmental justice	29	5
Public safety	28	5
Food security	22	4
City services	14	3

What dinner participants said

pre_barriers_to_equity<-dbGetQuery(con, "SELECT response, freq, percent FROM presurvey_barriers_to_equity WHERE percent > 30 OR percent < 7") 
#top 3 and bottom 5

  kable(pre_barriers_to_equity, digits=0, col.names=c(" ","Count","Percent (%)"))%>%
  kable_styling(bootstrap_options = "striped")%>%
        row_spec(1:3, bold = T, color ="white", background="#65B23E")%>%
        row_spec(4:6, bold=T, color= "white", background="#E53939")%>%
      add_header_above(c("What are the top 3 barriers to creating a more equitable and inclusive city?"=1," "=2))

What are the top 3 barriers to creating a more equitable and inclusive city?
	Count	Percent (%)
Housing affordability	58	52
Income	35	31
Racism and discrimination	34	30
Food security	6	5
Infrastructure	5	4
City services	3	3

Trust in City government

What community members said

trust<-dbGetQuery(con, "SELECT * FROM commsurvey_trust_la_city")

trust$question_text<-trimws(trust$question_text, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") #remove whitespace from 'question_text' column

##This creates the order we want for the 'question_text:

trust$factor<-factor(trust$question_text, ordered=TRUE, levels=c("How much do you trust LA City staff to treat people of all races and ethnicities equally?", "How much do you trust LA City to provide equal city services to all people?","How much do you trust LA City government to provide equal opportunities for all people to participate in city decision-making?"))

##This creates the order we want for 'response' 
trust$legend <- factor(trust$response, ordered = TRUE, levels=c("A great deal of trust","A fair amount of trust","Little trust","No trust at all"))

##final graph
ggplot(data=trust, aes(x=fct_rev(factor), y= percent, fill=legend), margin=list(r=500), length=1000) + geom_bar(stat="identity") + scale_x_discrete(name="", labels = function(x) str_wrap(x, width = 20)) + scale_y_continuous(name="", labels=scales::percent)+ geom_text(aes(label=freq), position=position_stack(vjust=.5), size = 3)+scale_fill_brewer(palette='Blues') + coord_flip()+labs(fill="")

What dinner participants said

presurvey_trust<-dbGetQuery(con, "SELECT * FROM presurvey_trust_la_city")

presurvey_trust$question_text<-trimws(presurvey_trust$question_text, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") #remove whitespace from 'question_text' column


##This creates the order we want for the 'question_text:
presurvey_trust$factor<-factor(presurvey_trust$question_text, ordered=TRUE, levels=c("How much do you trust LA City staff to treat people of all races and ethnicities equally?", "How much do you trust LA City to provide equal city services to all people?","How much do you trust LA City government to provide equal opportunities for all people to participate in city decision-making?"))

##This creates the order we want for 'response' 
presurvey_trust$legend <- factor(presurvey_trust$response, ordered = TRUE, levels=c("A great deal of trust","A fair amount of trust","Little trust","No trust at all"))

##final graph
ggplot(data=presurvey_trust, aes(x=fct_rev(factor), y= percent, fill=legend)) + geom_bar(stat="identity") + scale_x_discrete(name="", labels = function(x) str_wrap(x, width = 20)) + scale_y_continuous(name="", labels=scales::percent)+geom_text(aes(label=freq), position=position_stack(vjust=.5), size = 3)+scale_fill_brewer(palette='Blues')+coord_flip()

#plot_ly(presurvey_trust, x=~question_text, y=~percent, type='bar', orientation='h', name='question_text')%>%
 # layout(barmode='stack', xaxis=list(title=" "), yaxis=list(title=' '))

# pre_trust_x<-list(title="", tickformat="%")
# pre_trust_y<-list(title="")

#plot_ly(presurvey_trust, x = ~"No trust at all", y = ~question_text, type = 'bar', name = 'No trust at all', orientation='h') %>%add_trace(X = ~"Little trust", name = "Little trust") %>% add_trace(X = ~"A fair amount of trust", name = "A fair amount of trust") %>%  add_trace(X = ~"A great deal of trust", name = "A great deal of trust") %>%layout(xaxis = list(title = "",                    showgrid = FALSE,
  #                    showline = FALSE,
    #                  showticklabels = FALSE,
     #                 zeroline = FALSE),
      #   yaxis = list(title = "",
       #               showgrid = FALSE,
        #             showline = FALSE,
        #             showticklabels = FALSE,
          #            zeroline = FALSE),barmode = 'stack')

How much to prioritize equity and the ORE?

Over 4 out of 5 survey respondents (84%) and dinner participants (84%) believed creating greater equity and inclusivity in our city should be a HIGH priority for LA City government and officials.
Over 4 in 5 people attending our dinners (85%) supported or strongly supported created an Office of Racial Equity in LA City.

How much to prioritize equity

What community members said

equity_priority<-dbGetQuery(con, 'SELECT * FROM commsurvey_equity_priority')

#formatting

equity_priority_text<-c('n=451', 'n=74', 'n=15')
priority_x<-list(title="", tickformat="%")
priority_y<-list(title="")

#graph

plot_ly(equity_priority, x=~pct ,  y=~fct_rev(factor(equity_priority, levels=equity_priority)), type = 'bar', text=equity_priority, orientation='h')%>%
  layout(title= str_wrap("How much of a priority should creating greater equity and inclusivity in our city be to LA City government and officials?"), strwrap(equity_priority$pct, width=10), xaxis=priority_x, yaxis=priority_y)

What dinner participants said

pre_equity_priority<-dbGetQuery(con, 'SELECT * FROM presurvey_equity_priority')

pre_equity_priority$response<-trimws(pre_equity_priority$response, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") #remove whitespace from 'question_text' column
#formatting
pre_equity_priority$factor <- factor(pre_equity_priority$response, ordered = TRUE, levels=c("High priority","Somewhat of a priority","Not a priority"))

priority_x<-list(title="", tickformat="%")
priority_y<-list(title="")

#graph
plot_ly(pre_equity_priority, x=~percent, y=~fct_rev(factor), type ='bar', text=pre_equity_priority, orientation='h')%>%
  layout(title= str_wrap("How much of a priority should creating greater equity and inclusivity in our city be to LA City government and officials?"), xaxis=priority_x, yaxis=priority_y)

Degree of support for an ORE

What dinner participants said

post_support<-dbGetQuery(con, 'SELECT * FROM postsurvey_ore_support')
post_support$response<-trimws(post_support$response, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") #remove whitespace from 'question_text' column
#formatting
post_support$factor <- factor(post_support$response, ordered = TRUE, levels=c("Strongly support","Support", "Neither oppose or support", "Oppose", "Strongly oppose"))
support_x<-list(title="", tickformat="%")
support_y<-list(title="")
#graph

plot_ly(post_support, x=~percent, y=~fct_rev(factor), type ='bar', text=post_support, orientation='h')%>%
  layout(title= "How much do you support creating an Office of Racial Equity in LA City?", xaxis=support_x, yaxis=support_y)

What should an ORE do?

Community respondents most frequently thought the city should invest dollars based on community need (69%), invest in local minority-owned businesses (64%), and make grants to local organizations that are addressing racial equity (60%) in order to work toward greater equity in LA.
When asked to prioritize what the city should do first with an ORE, dinner participants prioritized applying equitable budgeting tools to make city investments based on need (19%).
When asked to rate the potential of ORE activities to impact racial equity, dinner participants most often rated local minority-owned business (76%), application of equitable budgeting tools (75%), hiring of diverse staff from local communities (69%), and grants to local community organizations (68%) as having a potentially “high” impact on racial equity.

What should LA City do

What community members said

#grab view and apply filters 

equity_activities<-dbGetQuery(con, "SELECT response, freq, percent FROM commsurvey_greater_equity WHERE percent > .6 or percent < .55 and percent > .44")

equity_activities$percent<-paste(round(equity_activities$percent*100,digits=0),"%",sep="") #change column from decimals to percents for percentage 

#Create table

  kable(equity_activities, digits=2, col.names=c("Response","Frequency","Percent"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    row_spec(1:3, bold = T, color ="white", background="#65B23E")%>%
    row_spec(4:7, bold=T, color= "white", background="#E53939")%>%
    add_header_above(c("What should LA City do about equity"=1," "=2))

What should LA City do about equity
Response	Frequency	Percent
City investments and dollars distributed based on community need	371	69%
Investments in local minority-owned businesses	343	64%
Grants to local community organizations that are addressing racial equity	325	60%
Conversations between City staff and community members about community needs	296	55%
Collecting and analyzing data on racial equity and sharing it with the public	291	54%
Opportunities and spaces for communities to come together on their own and solve issues	250	46%
Guided conversations between community members about race and racism	238	44%

ORE’s first step

What dinner participants said

post_first<-dbGetQuery(con, 'SELECT * FROM postsurvey_ore_first_action')
post_first_top<-dbGetQuery(con, "SELECT response, freq, percent FROM postsurvey_ore_first_action where percent>.10 or percent<.05")
post_first_top$percent<-paste(round(post_first_top$percent*100,digits=0),"%",sep="") #change column from decimals to percents for percentage 


  kable(post_first_top, digits=0, col.names=c("Activity","Count","Percent"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    row_spec(1:4, bold = T, color ="white", background="#3498DB")%>%
    row_spec(5:6, bold=T, color= "white", background="#737373")%>%
    add_header_above(c("What should an Office of Racial Equity do first? (top and bottom activities)"=1," "=2))

What should an Office of Racial Equity do first? (top and bottom activities)
Activity	Count	Percent
Application of equitable budgeting tools to make city investments based on need	15	19%
Hiring diverse city staff from local communities	9	11%
Investments in local minority-owned businesses	9	11%
Application of a Racial Equity Impact analysis for city officials to use when making policy decisions	8	10%
Grants to local community organizations that are addressing racial equity	3	4%
Trainings for city staff on implicit bias, race, and racism	2	3%

# keeping for good measure
#post_first$wrapped <- sapply(post_first$response, 
                     # FUN = function(question_text) {paste(strwrap(question_text, width = 40), collapse = "<br>")}) ##wrap text for y axis 

#post_x<-list(title="", tickformat="%")
#post_y<-list(title="")

#plotly graph

#plot_ly(post_first, x=~percent, y=~reorder(wrapped, percent), type ='bar', text=post_first, orientation='h', margin=list(l=400, b=200), #height=1000)%>%
#  layout(strwrap(post_first, width=20),title="What action should an Office of Racial Equity do first in LA city?", xaxis=post_x, yaxis=post_y)

Activities based on highest impact

What dinner participants said

post_impact<-dbGetQuery(con, 'SELECT * FROM postsurvey_action_impact')
post_high_impact <- filter(post_impact[order(-post_impact$percent) , ], response == "High impact")

 post_impact_trim<- subset(post_high_impact, select = c(question_text, freq, percent))
 post_impact_trim$percent<-paste(round(post_impact_trim$percent,digits=0),"%",sep="") #round percentage



  kable(post_impact_trim, digits=0, col.names=c("Activity","Count","Percent High Impact"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    row_spec(1:4, bold = T, color ="white", background="#3498DB")%>%
    row_spec(5:8, bold = T, color ="white", background="#DCDCDC")%>%
    row_spec(9:12, bold=T, color= "white", background="#808080")%>%
    add_header_above(c("What should an Office of Racial Equity do first? (top and bottom activities)"=1," "=2))

What should an Office of Racial Equity do first? (top and bottom activities)
Activity	Count	Percent High Impact
Investments in local minority-owned businesses.	61	1%
Application of equitable budgeting tools to make city investments based on community need.	59	1%
Hiring diverse city staff from local communities.	55	1%
Grants to local community organizations that are addressing racial equity.	54	1%
Application of a Racial Equity Impact analysis for city officials to use when making policy decisions.	50	1%
Collecting and analyzing data on racial equity and sharing it with the public.	50	1%
Trainings for city staff on implicit bias, race and racism.	49	1%
Trainings for community residents on how to participate in city government.	49	1%
Setting racial equity action plans that outline vision, strategies, and actions for the office and city departments.	47	1%
Guided conversations between community members about race and racism.	47	1%
Establishment of a community oversight commission for the Office of Racial Equity.	44	1%
Implementation of civic engagement standards to improve public participation in city decision-making.	44	1%

#post_x<-list(title="")
#post_y<-list(title="", labels = function (y) str_wrap(y, width = 20))
#post_high_impact$wrapped <- sapply(post_high_impact$question_text, 
                     # FUN = function(question_text) {paste(strwrap(question_text, width = 40), collapse = "<br>")}) ##wrap text for y axis 

#plot_ly(post_high_impact, x=~percent, y=~reorder(wrapped, percent), type ='bar', text=post_high_impact, orientation='h', margin=list(l=400, b=200), height=1000)%>%
 # layout(title="Potential ORE Functions based on Ratings of High Impact", xaxis=post_x, yaxis=post_y)

Demographics

Map of Survey Respondents

Respondents who live in LA

Zip codes of respondents who indicated they live in LA. Size of data represents number of individuals who reponded at that zip code

#get zip data
zips_living<-read.csv(file ="W:/Project/OSI/EmbRACELA/Data/CommunitySurvey/commsurvey_zips_living_geocoded.csv", header=TRUE, sep=",")

#zips_total<-read.csv(file = "commsurvey_zips_total_geocoded.csv", header=TRUE, sep=",")

# Making sure we are working with rows that don't have any blanks
zips_living <- zips_living[complete.cases(zips_living),]
#zips_total <- zips_total[complete.cases(zips_total),]

#Import city council districts shapefile
cds <- st_read(
  "W:/Project/OSI/EmbRACELA/Data/CommunitySurvey/CnclDist_July2012/CnclDist_July2012.shp")

## Reading layer `CnclDist_July2012' from data source `W:\Project\OSI\EmbRACELA\Data\CommunitySurvey\CnclDist_July2012\CnclDist_July2012.shp' using driver `ESRI Shapefile'
## Simple feature collection with 15 features and 10 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 6359578 ymin: 1715011 xmax: 6514633 ymax: 1945515
## epsg (SRID):    NA
## proj4string:    +proj=lcc +lat_1=34.03333333333333 +lat_2=35.46666666666667 +lat_0=33.5 +lon_0=-118 +x_0=2000000 +y_0=500000.0000000001 +datum=NAD83 +units=us-ft +no_defs

cds <- st_transform(cds, CRS("+proj=longlat +datum=WGS84 +no_defs"))
#cds <- spTransform(cds, CRS("+proj=longlat +ellps=GRS80"))


######map of council districts with zip codes of respondants who live in LA 
#######point data sized by count per zip code

leaflet(cds) %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.5)%>%
  addProviderTiles(providers$CartoDB.Positron)%>%
addCircleMarkers(data = zips_living, lng = ~X, lat = ~Y, 
                   weight = 1, radius = ~count, fillOpacity = .9,
                   color="blue")

Race and ethnicity

Community survey respondents

race <- dbGetQuery(con, "SELECT * FROM commsurvey_race") #grab race view from potsgres 
race_count<-c('n=6', 'n=45', 'n=123', 'n=161', 'n=6', 'n=49', 'n=137') #create hover text for n

#create formatting for x axis: no title, and decimals become whole percentages 
race_x<-list(
  title="",
  tickformat="%")
#create formatting for y axis: no title
race_y<-list(
  title=""
) 
##come back to this and figure out ordering! 

plot_ly(race, x = ~pct, y= ~(factor(race, levels=race)), type = 'bar', text = race_count, orientation='h')%>%
  layout(title = "Race and Ethnicity of Respondants", xaxis=race_x, yaxis = race_y) #use the race_x and race_y lists created earlier to implement our desired axis formatting

Dinner participants

pre_race <- dbGetQuery(con, "SELECT * FROM presurvey_raceethnicity_censuscomp") #grab race view from potsgres 

#create formatting for x axis: no title, and decimals become whole percentages 
pre_race_x<-list(
  title="",
 tickformat="%") 

#create formatting for y axis: no title
pre_race_y<-list(
title="") 

plot_ly(pre_race, x =~pct, y=~reorder(response, pct), type = 'bar')%>%
 layout(xaxis=pre_race_x, yaxis = pre_race_y) #use the race_x and race_y lists created earlier to implement our desired axis formatting

Length of tenure in LA

Community survey respondents

demographics<-dbGetQuery(con, "SELECT * FROM commsurvey_demographics")

live_work<-demographics[c(5:14), c(1:5)] #subset data to only get the rows for the live-work questions

#remove whitespace from 'question_text' column

live_work$question_text<-trimws(live_work$question_text, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") 

##This creates the order we want for the 'question_text:

live_work$factor<-factor(live_work$question_text, ordered=TRUE, levels=c("How long have you worked in LA City?", "How long have you lived in LA City?"))

##This creates the order we want for 'response' 
live_work$legend <- factor(live_work$response, ordered = TRUE, levels=c("Less than a year","1 to 5 years","6 to 10 years", "11 to 20 years","Over 20 years"))
##final graph
ggplot(data=live_work, aes(x=fct_rev(question_text), y= freq, fill=fct_rev(legend)), na.rm=TRUE) + geom_bar(stat="identity") + scale_x_discrete(name="", labels = function(x) str_wrap(x, width = 20)) + scale_y_continuous(name="") +  geom_text(aes(label = freq),
              position=position_stack(vjust = .5), size=3) + scale_fill_brewer("Blues")+ labs(title = "How many respondants live or work in Los Angeles", caption="Footnote: 57 respondants indicated that they attended school in Los Angeles.")+theme(legend.title=element_blank())

Dinner participants

pre_live_work_study<-dbGetQuery(con, 'SELECT * FROM presurvey_demographics')
pre_live_work <- filter(pre_live_work_study, question_text %in% c("How long have you lived in LA City?", "How long have you worked in LA City?"))

#remove whitespace from 'question_text' column
  pre_live_work$question_text<-trimws(pre_live_work$question_text, which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
  
##This creates the order we want for the 'question_text:

pre_live_work$factor<-factor(pre_live_work$question_text, ordered=TRUE, levels=c("How long have you worked in LA City?", "How long have you lived in LA City?"))

##This creates the order we want for 'response' 
pre_live_work$legend <- factor(pre_live_work$response, ordered = TRUE, levels=c("Less than a year","1 to 5 years","6 to 10 years","Over 10 years", "11 to 20 years","Over 20 years"))

##final graph
ggplot(data=pre_live_work, aes(x=fct_rev(question_text), y= freq, fill=fct_rev(legend)), na.rm=TRUE) + geom_bar(stat="identity") + scale_x_discrete(name="", labels = function(x) str_wrap(x, width = 20)) + scale_y_continuous(name="") +  geom_text(aes(label = freq),
              position=position_stack(vjust = .5), size=3) + scale_fill_brewer("Blues")+ labs(title = "How many respondants live or work in Los Angeles", caption="Footnote: 10 respondents indicated that they attended school in Los Angeles.")+theme(legend.title=element_blank())

Gender

Community survey respondents

gender<-dbGetQuery(con, 'SELECT * FROM commsurvey_gender')
gender_count<-c('n=364', 'n=154', 'n=15', 'n=2', 'n=1') ##manually setting hover text

gender_x<-list(title="", tickformat="%")
gender_y<-list(title="")



plot_ly(gender, x = ~pct, y=~fct_rev(factor(gender, levels=gender)), type = 'bar', text = gender_count, orientation='h')%>%
  layout(strwrap(gender, width=20), xaxis=gender_x, yaxis=gender_y)

Dinner participants

pre_demographics<-dbGetQuery(con, 'SELECT * FROM presurvey_demographics')

pre_gender <- filter(pre_demographics, question_text == "Which of the following best describes your gender?")
x<-list(title="", tickformat="%")
y<-list(title="")

plot_ly(pre_gender, x=~pct, y=~reorder(response, pct), type ='bar', text=pre_gender, orientation='h')%>%
  layout(strwrap(pre_gender, width=20), title="Which of the following best describes your gender?", xaxis=x, yaxis=y)

Age

Community survey respondents

age <- dbGetQuery(con, "SELECT * FROM commsurvey_age") #grab age view from potsgres 
age_count<-c('n=26', 'n=175', 'n=146', 'n=71', 'n=59', 'n=48')

#format for x and y axis 
age_x<-list(
  title="",
  tickformat="%"
) 

age_y<-list(title="")

##graph
plot_ly(age, x = ~pct, y= ~fct_rev(factor(age, level=age)), type = 'bar', text = age_count, orientation='h')%>%
  layout(xaxis=age_x, yaxis=age_y)

Dinner participants

pre_demographics<-dbGetQuery(con, 'SELECT * FROM presurvey_demographics')
pre_age <- filter(pre_demographics, question_text == "What is your age?")

#format for x and y axis 

pre_age_x<-list(
 title="",
  tickformat="%") 

pre_age_y<-list(title="")

##graph
plot_ly(pre_age, x = ~pct, y= ~fct_rev(factor(response, level=response)), type = 'bar', orientation='h')%>%
 layout(xaxis=pre_age_x, yaxis=pre_age_y)

Civic engagement activities

On average, people attending our dinners had participated in X civic engagement activities over the past year, more than community survey respondents

Community survey respondents

ce<-dbGetQuery(con, "SELECT response, freq, percent FROM commsurvey_ce_activities WHERE percent > .60")


ce$percent<-paste(round(ce$percent*100,digits=0),"%",sep="") #change column from decimals to percents for percentage 

#Create table

  kable(ce, col.names=c("Response","Frequency","Percent (%)"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    add_header_above(c("Top civic engagement activities are respondents involved with"=1," "=2))

Top civic engagement activities are respondents involved with
Response	Frequency	Percent (%)
Voted in an election	388	72%
Signed a petition in-person or online	372	69%
Volunteered in my community	347	64%

Dinner participants

pre_ce<-dbGetQuery(con, "SELECT response, freq, percent FROM presurvey_ce_activities")
pre_ce_top <- filter(pre_ce, percent>.66)



pre_ce_top$percent<-paste(round(pre_ce_top$percent*100,digits=0),"%",sep="") #change column from decimals to percents for percentage 


#Create table
  
  
  kable(pre_ce_top, col.names=c("Activity","Count","Percent (%)"))%>%
  kable_styling(bootstrap_options = "striped")%>%
    add_header_above(c("Top civic engagement activities participants were involved with"=1," "=2))

Top civic engagement activities participants were involved with
Activity	Count	Percent (%)
Volunteered in my community	92	82%
Attended a community meeting	92	82%
Attended a public meeting	87	78%
Met with a government representative	74	66%
Voted in an election	74	66%

Affiliation

Dinner participants

pre_demographics<-dbGetQuery(con, 'SELECT * FROM presurvey_demographics')
represented <- filter(pre_demographics, question_text == "Who are you representing today?")
x<-list(title="", tickformat="%")
y<-list(title="")

plot_ly(represented, x=~pct, y=~reorder(response, pct), type ='bar', text=represented, orientation='h')%>%
  layout(title="Who are you representing today?", xaxis=x, yaxis=y)

embRACE LA 2019

Results from Community Survey and Council Dinners

Advancement Project California

Overview

Vision for an equitable LA City

How equitable is LA City?

Is LA City equitable

What community members said

What dinner participants said

Top barriers to equity

What community members said.

What dinner participants said

Trust in City government

What community members said

What dinner participants said

How much to prioritize equity and the ORE?

How much to prioritize equity

What community members said

What dinner participants said

Degree of support for an ORE

What dinner participants said

What should an ORE do?

What should LA City do

What community members said

ORE’s first step

What dinner participants said

Activities based on highest impact

What dinner participants said

Demographics

Map of Survey Respondents

Respondents who live in LA

Race and ethnicity

Community survey respondents

Dinner participants

Length of tenure in LA

Community survey respondents

Dinner participants

Gender

Community survey respondents

Dinner participants

Age

Community survey respondents

Dinner participants

Civic engagement activities

Community survey respondents

Dinner participants

Affiliation

Dinner participants